Three-dimensional parametric design method and system for intelligent round baler

Abstract

The present invention relates to the field of three-dimensional design technology, and in particular to a three-dimensional parametric design method and system for intelligent round balers. The method comprises the following steps: obtaining intelligent round baler design data; compressing redundant features of the intelligent round baler design data to generate simplified design data of the intelligent round baler; standardizing the simplified design data of the intelligent round baler to generate simplified design data of standard intelligent round balers; extracting three-dimensional geometric features of the simplified design data of the standard intelligent round baler to obtain three-dimensional geometric feature data of the intelligent round baler; calculating multi-dimensional structural characteristics of the three-dimensional geometric feature data of the intelligent round baler to generate multi-dimensional structural mechanical characteristic data of the intelligent round baler; and constructing a parametric design model. The present invention improves the redundancy, efficiency and accuracy in the three-dimensional parametric design of intelligent round balers through data simplification, linkage between geometric and mechanical characteristics, combination of material characteristics and virtual simulation feedback optimization.

Description

Technical Field

[0001] The present invention relates to the field of three-dimensional design technology, and in particular to a three-dimensional parameterized design method and system for an intelligent round baler. Background Art

[0002] Early round balers were relatively simple in design, relying mainly on mechanical transmission to tie bales, but they had many limitations in terms of efficiency, accuracy and bale quality control. With the development of computer technology, sensor technology and automatic control technology, intelligent round balers have gradually applied electronic control systems and intelligent sensors, which can automatically detect the status of bales and perform precise control. Innovations at this stage have significantly improved the operating efficiency of the equipment and the quality of bales. The introduction of three-dimensional parametric design technology has brought new breakthroughs in the design of intelligent round balers. Through three-dimensional modeling and parametric design, the size, shape and assembly of each component can be more accurately controlled, thereby optimizing the structural performance of the entire machine. In addition, parametric design makes adjustments under different working conditions more flexible and adapts to diversified agricultural needs. At the same time, the development of digital twin technology enables designers to simulate and optimize performance in a virtual environment, effectively shortening the design cycle and improving product reliability. However, current traditional technologies often fail to fully consider the mechanical properties of the structure when extracting three-dimensional geometric features, resulting in poor performance of the designed mechanical structure in actual use. At the same time, existing design methods often ignore the impact of material properties on the design results, resulting in insufficient strength or over-design problems during the manufacturing process, which in turn leads to low design accuracy and efficiency. Summary of the invention

[0003] Based on this, it is necessary to provide a three-dimensional parametric design method and system for an intelligent round baler to solve at least one of the above technical problems.

[0004] To achieve the above object, a three-dimensional parametric design method for an intelligent round baler is provided, the method comprising the following steps:

[0005] Step S1: acquiring intelligent round baler design data; compressing redundant features of the intelligent round baler design data to generate simplified design data of the intelligent round baler; and standardizing the simplified design data of the intelligent round baler to generate simplified design data of standard intelligent round baler;

[0006] Step S2: extracting three-dimensional geometric features from the simplified design data of the standard intelligent round baler to obtain three-dimensional geometric feature data of the intelligent round baler; calculating multi-dimensional structural characteristics of the three-dimensional geometric feature data of the intelligent round baler to generate multi-dimensional structural mechanical characteristic data of the intelligent round baler; constructing a parametric design model; performing nonlinear parameter mapping on the parametric design model using the multi-dimensional structural mechanical characteristic data of the intelligent round baler to generate parametric constrained optimization data;

[0007] Step S3: Analyze the design material characteristics of the simplified design data of the standard intelligent round baler to generate the material characteristic data of the intelligent round baler; perform virtual modeling of parts using the three-dimensional geometric feature data of the intelligent round baler, the multi-dimensional structural mechanical characteristic data of the intelligent round baler, and the material characteristic data of the intelligent round baler based on the parametric constraint optimization data to generate the three-dimensional modeling data of the parts of the intelligent round baler; dynamically reconstruct the three-dimensional virtual model of the three-dimensional modeling data of the parts of the intelligent round baler to generate a three-dimensional virtual simulation model of the intelligent round baler;

[0008] Step S4: Perform virtual manufacturing simulation on the three-dimensional virtual simulation model of the intelligent round baler according to the three-dimensional virtual simulation model of the intelligent round baler to generate virtual manufacturing feedback data; output the three-dimensional parametric design model of the intelligent round baler as a programmable model file through the virtual manufacturing feedback data, thereby generating a three-dimensional intelligent design document.

[0009] The present invention effectively reduces the amount of unnecessary data and improves the efficiency of data processing by compressing redundant design features; data standardization enhances data consistency, ensures the accuracy of subsequent geometric feature extraction and parametric design, and reduces design deviations and error propagation. Three-dimensional geometric feature extraction enables the geometric shape information of the round baler to be fully captured, providing a basis for subsequent design; multi-dimensional structural mechanical property calculation improves the model's ability to predict stress and deformation responses under complex working conditions; the design model is optimized through nonlinear parameter mapping, so that it can adapt to different working conditions and improve the reliability and durability of the round baler design. Material property analysis ensures that the strength, toughness and fatigue performance of different components during use match the design requirements, reducing over-design or material waste; part virtual modeling and dynamic reconstruction of three-dimensional virtual models help designers quickly iterate and optimize in a virtual environment, avoid design errors in physical manufacturing, and shorten the design verification cycle. Virtual manufacturing simulation can simulate the operating steps, equipment performance and problems in the actual manufacturing process, discover and optimize problems in manufacturing in advance, and reduce rework; optimize the design through simulation feedback data, so that design and manufacturing are closely integrated, and the generated programmable model files are directly used for manufacturing equipment, shortening the conversion cycle from design to manufacturing, improving production efficiency and reducing costs. Therefore, the present invention improves the redundancy, efficiency and accuracy in the three-dimensional parametric design of intelligent round balers through data simplification, linkage between geometric and mechanical properties, combination of material properties and virtual simulation feedback optimization.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Acquire intelligent round baler design data;

[0012] Step S12: performing data cleaning on the intelligent round baler design data to generate intelligent round baler design cleaning data; performing outlier filtering on the intelligent round baler design cleaning data to generate intelligent round baler design filtering data;

[0013] Step S13: performing data dimensionality reduction on the intelligent round baler design filtering data to generate intelligent round baler design dimensionality reduction data; performing redundant feature compression on the intelligent round baler design dimensionality reduction data to generate intelligent round baler simplified design data;

[0014] Step S14: Standardizing the simplified design data of the intelligent round baler to generate simplified design data of the standard intelligent round baler.

[0015] The present invention ensures that the data used is more accurate and reliable, and reduces the negative impact on the design results through data cleaning and outlier filtering. Data dimension reduction and redundant feature compression can reduce data dimensions, retain key features, and improve the efficiency of subsequent analysis and model training. Data standardization allows data from different sources to be compared under the same standard, enhancing the comparability and consistency of design data. By streamlining design data, computing resource consumption is reduced, and the speed of data processing and analysis is accelerated. Providing higher quality design data allows designers to make decisions based on more accurate information, thereby improving the effectiveness and reliability of the design. Standardized data is easier to share with other systems or teams, promoting cross-team collaboration and information exchange.

[0016] Preferably, step S2 comprises the following steps:

[0017] Step S21: extracting three-dimensional geometric features from the simplified design data of the standard intelligent round baler to obtain three-dimensional geometric feature data of the intelligent round baler, wherein the three-dimensional geometric feature extraction includes shape features, size features and curvature features;

[0018] Step S22: performing multi-dimensional structural characteristic calculation on the three-dimensional geometric characteristic data of the intelligent round baler to generate multi-dimensional structural mechanical characteristic data of the intelligent round baler;

[0019] Step S23: performing high-dimensional spatial feature optimization on the simplified design data of the standard intelligent round baler according to the three-dimensional geometric feature data of the intelligent round baler and the multi-dimensional structural mechanical property data of the intelligent round baler to generate high-dimensional spatial feature data; performing dynamic feature mapping on the high-dimensional spatial feature data to generate multi-dimensional spatial optimization feature data of the intelligent round baler;

[0020] Step S24: construct a parametric design model based on the simplified design data of the standard intelligent round baler; use the multi-dimensional space optimization feature data of the intelligent round baler to perform nonlinear parameter mapping on the parametric design model to generate parametric constraint optimization data.

[0021] Through the extraction of three-dimensional geometric features, the present invention can fully understand the shape, size and curvature characteristics of the intelligent round baler, and improve the accuracy and pertinence of the design. The calculation of multi-dimensional structural characteristics makes the design data not only limited to geometric information, but also includes mechanical properties, enhancing the ability to evaluate the strength and stability of the design. High-dimensional space feature optimization can discover potential problems in the design and make improvements, improving the performance and adaptability of the overall design. Dynamic feature mapping allows the design model to be adjusted in real time as changes occur, improving the flexibility and responsiveness of the design. The parametric design model built based on standard data can quickly adapt to different design requirements and improve the efficiency and accuracy of the design. The parametric constraint optimization data generated by nonlinear parameter mapping helps to ensure the effectiveness of the design under various conditions and improve the safety and practicality of the design. Providing multi-dimensional space optimization feature data can provide designers with new ideas and perspectives and encourage the realization of innovative designs.

[0022] Preferably, step S22 includes the following steps:

[0023] Step S221: performing mechanical property correlation analysis on the three-dimensional geometric feature data of the intelligent round baler to generate geometric feature mechanical mapping data;

[0024] Step S222: performing structural characteristic hierarchical analysis on the geometric characteristic mechanical mapping data to generate structural characteristic hierarchical data of the intelligent round baler; performing multi-layer mechanical characteristic calculation on the structural characteristic hierarchical data of the intelligent round baler to obtain multi-layer structural mechanical characteristic data of the intelligent round baler, wherein the multi-layer mechanical characteristic calculation includes local stress calculation, bending strength calculation and dynamic load calculation;

[0025] Step S223: Performing mechanical environment simulation on the mechanical characteristic data of the multi-layer structure of the intelligent round baler to generate mechanical environment simulation data;

[0026] Step S224: Calibrate the overall mechanical characteristics of the multi-layer structure mechanical characteristics data of the intelligent round baler according to the mechanical environment simulation data, so as to generate the multi-dimensional structural mechanical characteristics data of the intelligent round baler.

[0027] The present invention helps designers understand the relationship between geometric features and mechanical properties through mechanical property correlation analysis, thereby optimizing the design. Through hierarchical analysis of structural characteristics, structural characteristics at different levels can be more clearly identified, which helps to formulate more targeted improvement plans. Multi-layer mechanical property calculations (such as local stress, bending strength, and dynamic load) provide a comprehensive performance evaluation of the intelligent round baler under different working conditions. Mechanical environment simulation can predict the performance of the intelligent round baler in the actual use environment and ensure the adaptability and safety of the design. Overall mechanical property calibration helps to eliminate the deviation between simulation and actual conditions and generate more accurate multi-dimensional structural mechanical property data. Based on more reliable data, designers can make more scientific decisions, reduce design risks, and improve the market competitiveness of products. Through accurate mechanical property data, the design can be better applied in actual engineering to improve the performance and reliability of the intelligent round baler.

[0028] Preferably, step S24 includes the following steps:

[0029] Step S241: extracting key part features from the simplified design data of the standard intelligent round baler to obtain key part feature data of the intelligent round baler; performing spatial mapping on the key part feature data of the intelligent round baler to generate a parametric design model;

[0030] Step S242: performing nonlinear parameter mapping on the multi-dimensional spatial optimization characteristic data of the intelligent round baler to generate spatial parameter mapping data of the intelligent round baler;

[0031] Step S243: introducing physical constraints into the spatial parameter mapping data of the intelligent round baler to generate physical constraint data of the intelligent round baler, wherein the physical constraint introduction includes the introduction of load capacity limitation, the introduction of running speed limitation and the introduction of geometric size limitation;

[0032] Step S244: Based on the physical constraint data of the intelligent round baler, the intelligent round baler space parameter mapping data is used to tune the design parameters of the parametric design model to generate parametric constraint optimization data.

[0033] Through the feature extraction of key parts, the present invention enables designers to focus on the key parts of the intelligent round baler and ensure that their importance is fully considered in the design. The parametric design model generated by spatial mapping can quickly adapt to different design requirements and improve the flexibility and efficiency of the design. Nonlinear parameter mapping allows designers to explore the design space in depth and find better design solutions, thereby improving the overall performance of the intelligent round baler. The introduction of physical constraints such as load capacity, operating speed and geometric dimensions ensures that the design meets engineering requirements in practical applications and improves the reliability of the design. Parameter tuning based on the physical constraint data of the intelligent round baler makes the design process more scientific and data-driven, reducing reliance on empiricism. Through systematic constraint optimization, the design iteration process can be accelerated, the design cycle can be shortened, and work efficiency can be improved. After considering the physical constraints, the designed intelligent round baler can better meet safety standards in practical applications, reduce the risk of failure, and enhance user trust.

[0034] Preferably, step S3 comprises the following steps:

[0035] Step S31: performing design material property analysis on the simplified design data of the standard intelligent round baler to generate material property data of the intelligent round baler;

[0036] Step S32: Based on the parameterized constraint optimization data, the three-dimensional geometric feature data of the intelligent round baler, the multi-dimensional structural mechanical property data of the intelligent round baler, and the material property data of the intelligent round baler are used to perform virtual modeling of the parts of the intelligent round baler to generate three-dimensional modeling data of the parts of the intelligent round baler;

[0037] Step S33: performing dynamic characteristic simulation on the three-dimensional modeling data of the intelligent round baler parts to generate dynamic characteristic simulation data; performing operating environment simulation on the dynamic characteristic simulation data to generate intelligent round baler modeling environment adaptability data;

[0038] Step S34: dynamically reconstructing the three-dimensional virtual model of the three-dimensional modeling data of the parts of the intelligent round baler through the modeling environment adaptability data and dynamic characteristic simulation data of the intelligent round baler to generate a three-dimensional virtual simulation model of the intelligent round baler.

[0039] The present invention ensures the selection of the most suitable material to improve the performance and durability of the intelligent round baler by analyzing the design material properties. Virtual modeling of parts is performed based on parametric constraint optimization data and other characteristic data to ensure that the three-dimensional model can truly reflect the design intent and improve the accuracy of the design. Dynamic characteristic simulation can predict the performance of the intelligent round baler in actual operation, helping designers to identify potential problems and optimize them. Operating environment simulation provides an assessment of the adaptability of the modeling environment to ensure the reliability and stability of the intelligent round baler under different conditions of use. By dynamically reconstructing the three-dimensional virtual model, design changes can be reflected in real time, improving the flexibility and response speed of the design. Comprehensive material properties, structural properties and dynamic data are used to promote experts from different fields to collaborate in the design process and improve the design quality. The optimized and verified three-dimensional virtual simulation model helps to accelerate product launch and improve the competitiveness of intelligent round balers in the market.

[0040] Preferably, performing dynamic boundary simulation on material property mapping data of the intelligent round baler according to multi-dimensional structural mechanical property data of the intelligent round baler through parameterized constraint optimization data includes:

[0041] According to the multi-dimensional structural mechanical characteristics data of the intelligent round baler, the material attribute mapping data of the intelligent round baler is matched with the component materials to generate the material matching data of the parts of the intelligent round baler;

[0042] Perform core part screening on the material matching data of the parts of the intelligent round baler to obtain the core part screening data of the intelligent round baler, wherein the core part screening data of the intelligent round baler includes the tying part data, the driving part data and the supporting structure part data;

[0043] The drum force characteristics of the bundling part data are simulated by using the parameterized constraint optimization data to generate the drum force characteristics simulation data; the gear vibration characteristics of the driving part data are simulated based on the parameterized constraint optimization data to generate the gear force characteristics simulation data; the bracket response characteristics of the supporting structure part data are simulated by using the parameterized constraint optimization data to generate the bracket response characteristics simulation data;

[0044] The simulation data of the drum force characteristics, the gear force characteristics and the bracket response characteristics are integrated to generate three-dimensional modeling data of the intelligent round baler parts.

[0045] The present invention ensures the selection of the most suitable material through component material matching, improves the overall performance and durability of the intelligent round baler, and reduces the failure rate. Core part screening helps designers focus on the most important structure, improves the pertinence and effectiveness of the design, and thus optimizes performance. The characteristics of the bundling part, drive part and support structure part are simulated to provide detailed force analysis to help identify potential design defects. The simulation data of different parts are integrated to form comprehensive three-dimensional modeling data, making design decisions more scientific and reliable. By simulating the response characteristics of rollers, gears and brackets, the dynamic performance of the structure in actual use can be predicted in advance to ensure the safety and stability of the design. The application of parametric constraint optimization data enables the design process to quickly adapt to different working conditions and requirements, and enhances the flexibility of the design. The systematic simulation and integration process accelerates design iterations, reduces design time, and improves overall work efficiency.

[0046] Preferably, step S4 comprises the following steps:

[0047] Step S41: Modeling and designing the parametric design model according to the three-dimensional virtual simulation model of the intelligent round baler to generate a three-dimensional parametric design model of the intelligent round baler;

[0048] Step S42: performing production adaptability detection on the three-dimensional parametric design model of the intelligent round baler to generate three-dimensional parameter production adaptability data; performing virtual manufacturing simulation on the three-dimensional parametric design model of the intelligent round baler through the three-dimensional parameter production adaptability data to generate virtual manufacturing feedback data;

[0049] Step S43: Outputting the three-dimensional parametric design model of the intelligent round baler into a programmable model file through virtual manufacturing feedback data, thereby generating a three-dimensional intelligent design document.

[0050] The present invention ensures that the geometric and mechanical properties of the model are more technically accurate and reduces design defects by associating three-dimensional virtual simulation with parametric design models. Production adaptability detection can identify potential conflicts between the design and the actual manufacturing process, thereby optimizing the production process and improving process stability. Virtual manufacturing simulation technology provides an effective method to predict and solve problems in manufacturing, improving the effectiveness and practicality of simulation models. Through virtual manufacturing feedback data, designers can make adjustments based on real-time data to improve the flexibility and responsiveness of the design. Programmable model file output realizes the automatic generation of design documents, improves the accuracy and consistency of documents, reduces errors caused by manual intervention, and the generated three-dimensional intelligent design documents provide unified technical standards, which promotes standardized management of design and production links.

[0051] In this specification, a three-dimensional parametric design system for an intelligent round baler is provided, which is used to execute the above-mentioned three-dimensional parametric design method for an intelligent round baler. The three-dimensional parametric design system for an intelligent round baler includes:

[0052] A data processing module is used to obtain the design data of the intelligent round baler; compress the redundant features of the design data of the intelligent round baler to generate the simplified design data of the intelligent round baler; and standardize the simplified design data of the intelligent round baler to generate the simplified design data of the standard intelligent round baler;

[0053] The structural mapping module is used to extract the three-dimensional geometric features of the simplified design data of the standard intelligent round baler to obtain the three-dimensional geometric feature data of the intelligent round baler; perform multi-dimensional structural characteristic calculations on the three-dimensional geometric feature data of the intelligent round baler to generate multi-dimensional structural mechanical characteristic data of the intelligent round baler; construct a parametric design model; use the multi-dimensional structural mechanical characteristic data of the intelligent round baler to perform nonlinear parameter mapping on the parametric design model to generate parametric constraint optimization data;

[0054] The three-dimensional reconstruction module is used to analyze the design material characteristics of the simplified design data of the standard intelligent round baler and generate the material characteristic data of the intelligent round baler; based on the parametric constraint optimization data, the three-dimensional geometric feature data of the intelligent round baler, the multi-dimensional structural mechanical characteristic data of the intelligent round baler and the material characteristic data of the intelligent round baler are used to perform virtual modeling of the parts and generate the three-dimensional modeling data of the parts of the intelligent round baler; the three-dimensional virtual model is dynamically reconstructed for the three-dimensional modeling data of the parts of the intelligent round baler to generate a three-dimensional virtual simulation model of the intelligent round baler;

[0055] The feedback output module is used to perform virtual manufacturing simulation on the three-dimensional virtual simulation model of the intelligent round baler according to the three-dimensional virtual simulation model of the intelligent round baler, and generate virtual manufacturing feedback data; the three-dimensional parametric design model of the intelligent round baler is output as a programmable model file through the virtual manufacturing feedback data, thereby generating a three-dimensional intelligent design document.

[0056] The beneficial effect of the present invention is that the design data of the intelligent round baler is simplified through redundant feature compression and data standardization, making subsequent processing more efficient, reducing the waste of computing resources caused by redundant information, and improving data processing speed and design accuracy. The three-dimensional geometric feature extraction and multi-dimensional structural mechanical property calculation ensure the accuracy of the shape, size and mechanical properties of the intelligent round baler, and provide reliable basic data for subsequent model optimization. The parametric design model is nonlinearly mapped based on the mechanical property data to generate parametric constraint optimization data. This process improves the flexibility and adaptability of the design model and ensures its optimized performance under different conditions. The material properties, geometric properties and structural mechanical properties are comprehensively applied to virtual modeling to generate accurate three-dimensional modeling data, ensuring the authenticity and physical consistency of the model, which helps to improve the structural strength and durability of the product. The dynamic reconstruction of the three-dimensional virtual model of the intelligent round baler provides a visual verification method for the product. Through real-time simulation and dynamic reconstruction, the behavior under complex operating environments can be simulated, providing important feedback for further optimization of the design. Through virtual manufacturing simulation, potential problems in the manufacturing process can be discovered in advance, the generation of design defects can be reduced, and the generated virtual manufacturing feedback data provides a reliable basis for optimizing the design model, improving manufacturing accuracy and product quality. By generating three-dimensional intelligent design documents through virtual manufacturing feedback, automated output is achieved, errors in manual document compilation are reduced, and standardized, easy-to-understand technical documents are provided for production, design, quality inspection and other links. The entire process from design, optimization, modeling, simulation to feedback and document output forms a closed-loop design optimization process, which improves the design efficiency, manufacturing accuracy and production adaptability of intelligent round balers. Therefore, the present invention improves the redundancy, efficiency and accuracy in the three-dimensional parametric design of intelligent round balers through data simplification, linkage between geometric and mechanical properties, combination of material properties and virtual simulation feedback optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A schematic diagram of the steps of a three-dimensional parametric design method for an intelligent round baler;

[0058] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0059] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0060] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0061] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0062] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0063] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0064] To achieve this, please refer to Figures 1 to 3 , a three-dimensional parametric design method for an intelligent round baler, the method comprising the following steps:

[0065] Step S1: acquiring intelligent round baler design data; compressing redundant features of the intelligent round baler design data to generate simplified design data of the intelligent round baler; and standardizing the simplified design data of the intelligent round baler to generate simplified design data of standard intelligent round baler;

[0066] Step S2: extracting three-dimensional geometric features from the simplified design data of the standard intelligent round baler to obtain three-dimensional geometric feature data of the intelligent round baler; calculating multi-dimensional structural characteristics of the three-dimensional geometric feature data of the intelligent round baler to generate multi-dimensional structural mechanical characteristic data of the intelligent round baler; constructing a parametric design model; performing nonlinear parameter mapping on the parametric design model using the multi-dimensional structural mechanical characteristic data of the intelligent round baler to generate parametric constrained optimization data;

[0067] Step S3: Analyze the design material characteristics of the simplified design data of the standard intelligent round baler to generate the material characteristic data of the intelligent round baler; perform virtual modeling of parts using the three-dimensional geometric feature data of the intelligent round baler, the multi-dimensional structural mechanical characteristic data of the intelligent round baler, and the material characteristic data of the intelligent round baler based on the parametric constraint optimization data to generate the three-dimensional modeling data of the parts of the intelligent round baler; dynamically reconstruct the three-dimensional virtual model of the three-dimensional modeling data of the parts of the intelligent round baler to generate a three-dimensional virtual simulation model of the intelligent round baler;

[0068] Step S4: Perform virtual manufacturing simulation on the three-dimensional virtual simulation model of the intelligent round baler according to the three-dimensional virtual simulation model of the intelligent round baler to generate virtual manufacturing feedback data; output the three-dimensional parametric design model of the intelligent round baler as a programmable model file through the virtual manufacturing feedback data, thereby generating a three-dimensional intelligent design document.

[0069] The present invention effectively reduces the amount of unnecessary data and improves the efficiency of data processing by compressing redundant design features; data standardization enhances data consistency, ensures the accuracy of subsequent geometric feature extraction and parametric design, and reduces design deviations and error propagation. Three-dimensional geometric feature extraction enables the geometric shape information of the round baler to be fully captured, providing a basis for subsequent design; multi-dimensional structural mechanical property calculation improves the model's ability to predict stress and deformation responses under complex working conditions; the design model is optimized through nonlinear parameter mapping, so that it can adapt to different working conditions and improve the reliability and durability of the round baler design. Material property analysis ensures that the strength, toughness and fatigue performance of different components during use match the design requirements, reducing over-design or material waste; part virtual modeling and dynamic reconstruction of three-dimensional virtual models help designers quickly iterate and optimize in a virtual environment, avoid design errors in physical manufacturing, and shorten the design verification cycle. Virtual manufacturing simulation can simulate the operating steps, equipment performance and problems in the actual manufacturing process, discover and optimize problems in manufacturing in advance, and reduce rework; optimize the design through simulation feedback data, so that design and manufacturing are closely integrated, and the generated programmable model files are directly used for manufacturing equipment, shortening the conversion cycle from design to manufacturing, improving production efficiency and reducing costs. Therefore, the present invention improves the redundancy, efficiency and accuracy in the three-dimensional parametric design of intelligent round balers through data simplification, linkage between geometric and mechanical properties, combination of material properties and virtual simulation feedback optimization.

[0070] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a three-dimensional parametric design method for an intelligent round baler according to the present invention. In this example, the three-dimensional parametric design method for an intelligent round baler includes the following steps:

[0071] Step S1: acquiring intelligent round baler design data; compressing redundant features of the intelligent round baler design data to generate simplified design data of the intelligent round baler; and standardizing the simplified design data of the intelligent round baler to generate simplified design data of standard intelligent round baler;

[0072] In the embodiment of the present invention, the original design data of the intelligent round baler is obtained, including but not limited to the functional requirements, geometric structure, material selection, manufacturing process, assembly requirements, etc. of the round baler. The design data comes from CAD models, engineering drawings, design documents, parameter tables, etc. Ensure the diversity and accuracy of data sources and fully cover all kinds of information required for the design. Perform feature analysis on the obtained intelligent round baler design data to identify redundant features. Redundant features include repeated information, invalid design data, or features that are irrelevant to the target performance. Algorithmic tools (such as principal component analysis PCA, feature selection algorithm, etc.) are used to compress redundant features and reduce unnecessary data redundancy to improve the efficiency of data processing and the accuracy of design optimization. After the redundant features are compressed, the simplified design data of the intelligent round baler is generated, the key design features are retained, and the amount of data is reduced. Standardization is performed on the simplified design data of the intelligent round baler, and the data format and unit are unified to ensure that all kinds of design data are consistent and comparable. The content of the standardization process includes data normalization, unit conversion, format normalization, etc., to ensure that the data can adapt to the subsequent calculation, optimization and simulation processes. Through data standardization, simplified design data of standard intelligent round balers is generated to provide standardized input for subsequent modeling and simulation.

[0073] Step S2: extracting three-dimensional geometric features from the simplified design data of the standard intelligent round baler to obtain three-dimensional geometric feature data of the intelligent round baler; calculating multi-dimensional structural characteristics of the three-dimensional geometric feature data of the intelligent round baler to generate multi-dimensional structural mechanical characteristic data of the intelligent round baler; constructing a parametric design model; performing nonlinear parameter mapping on the parametric design model using the multi-dimensional structural mechanical characteristic data of the intelligent round baler to generate parametric constrained optimization data;

[0074] In an embodiment of the present invention, the geometric features of the round baler, such as shape, size, curvature, etc., are analyzed by extracting three-dimensional geometric features from the simplified design data of the standard intelligent round baler. The geometric features may include the three-dimensional shapes of components such as the round baler drum, drive system, and support structure. The design data is analyzed using a geometric feature extraction algorithm (such as edge detection, surface fitting, etc.) to generate three-dimensional geometric feature data of the intelligent round baler. The three-dimensional geometric feature data of the intelligent round baler is used to calculate the multi-dimensional structural mechanical properties. This calculation includes structural mechanical properties such as local stress, stiffness of the overall structure, bending strength, and dynamic load. Finite element analysis (FEA) or multi-body dynamics simulation (MBS) is used to calculate the mechanical properties of different components of the intelligent round baler to generate multi-dimensional structural mechanical property data of the intelligent round baler. The results of the mechanical property calculation will cover the force analysis and deformation of the entire round baler under different working conditions to ensure the reliability of the design. A parametric design model is constructed based on the three-dimensional geometric feature data and multi-dimensional structural mechanical property data of the intelligent round baler. This model can automatically adjust according to changes in input parameters (such as size, material properties, and load conditions) to generate corresponding design solutions. Parametric modeling tools (such as CAD parametric modeling software) are used to associate geometric features with mechanical properties, so that the model has dynamic response characteristics. The parametric design model is the core foundation for subsequent design optimization and simulation testing. The multi-dimensional structural mechanical property data of the intelligent round baler is combined with the parametric design model for nonlinear parameter mapping. The purpose of nonlinear parameter mapping is to adjust the design parameters of the model through complex nonlinear relationships to achieve the best performance. The mapping data is analyzed to optimize the design parameters of the round baler, for example, by adjusting the roller diameter, gear ratio, support structure strength, etc., to improve the mechanical properties and work efficiency of the equipment. Parametric constraint optimization data is generated through nonlinear optimization algorithms (such as genetic algorithms, particle swarm algorithms, etc.). This optimization data can guide the round baler to maintain the best operating state under various working conditions while meeting manufacturing and use requirements.

[0075] Step S3: Analyze the design material characteristics of the simplified design data of the standard intelligent round baler to generate the material characteristic data of the intelligent round baler; perform virtual modeling of parts using the three-dimensional geometric feature data of the intelligent round baler, the multi-dimensional structural mechanical characteristic data of the intelligent round baler, and the material characteristic data of the intelligent round baler based on the parametric constraint optimization data to generate the three-dimensional modeling data of the parts of the intelligent round baler; dynamically reconstruct the three-dimensional virtual model of the three-dimensional modeling data of the parts of the intelligent round baler to generate a three-dimensional virtual simulation model of the intelligent round baler;

[0076] In the embodiment of the present invention, the material properties of each key component of the round baler are analyzed, including strength, hardness, corrosion resistance and density. Different materials can be selected for different components (such as rollers, brackets, gears, etc.). According to the design requirements, suitable materials are selected, and the mechanical properties of the materials (such as Young's modulus, yield strength, etc.) are quantitatively analyzed to generate material property data of the intelligent round baler. Material property analysis is performed through the material database and simulation software to ensure that the selected materials can meet the use requirements of the round baler under different working conditions. Virtual modeling of the components of the intelligent round baler is performed based on the following data: the parameterized constraint optimization data generated in step S2 is used to guide the design scheme; the overall shape and key components of the round baler are designed according to the three-dimensional geometric features; the structural design of each component is optimized according to the mechanical property data to ensure its stability under force and deformation; and the appropriate material model is selected for modeling in combination with the material properties. Computer-aided design (CAD) and finite element analysis (FEA) tools are used to perform detailed three-dimensional modeling of the key parts of the intelligent round baler to generate three-dimensional model data of each component (for example, rollers, gears, support frames, etc.). The results of virtual modeling will generate three-dimensional modeling data of the parts of the intelligent round baler. Dynamically reconstruct the three-dimensional modeling data of the intelligent round baler, including dynamic force simulation and operating environment simulation. The specific implementation method is as follows: Through multi-body dynamics (MBS) simulation and finite element analysis (FEA), the key components of the intelligent round baler are dynamically simulated, the force and motion behavior in actual operation are simulated, and dynamic characteristic simulation data is generated. Simulate the operation of the round baler in different working environments, such as the operation under different loads, speeds, and ground conditions, and generate modeling environment adaptability data. Integrate the dynamic simulation data with the operating environment data, optimize and adjust the three-dimensional modeling data of the round baler, and ensure that the virtual model can dynamically respond to operational changes under real working conditions. By combining the dynamically reconstructed modeling data and simulation results, a three-dimensional virtual simulation model of the intelligent round baler is constructed. This model will reflect the overall structure, material properties, force characteristics and dynamic response characteristics of the round baler. The three-dimensional virtual simulation model can be used for further design verification and optimization, such as performance testing, fault analysis and simulation of usage scenarios through simulation software.

[0077] Step S4: Perform virtual manufacturing simulation on the three-dimensional virtual simulation model of the intelligent round baler according to the three-dimensional virtual simulation model of the intelligent round baler to generate virtual manufacturing feedback data; output the three-dimensional parametric design model of the intelligent round baler as a programmable model file through the virtual manufacturing feedback data, thereby generating a three-dimensional intelligent design document.

[0078] In the embodiment of the present invention, the manufacturing process of the intelligent round baler is simulated by using a three-dimensional virtual simulation model. This process can be realized by digital twin technology (Digital Twin), including material procurement, parts processing, assembly process, quality inspection and other links. The process flow of the intelligent round baler in actual production is simulated, including key process steps such as welding, assembly, coating, etc., and detailed process simulation data is generated. The feasibility of each component in the actual manufacturing process is tested by virtual simulation, the adaptability of the production equipment is evaluated, the processing tolerance, material requirements, production time, etc. are checked, and the designed model can be manufactured smoothly. The simulation results generate virtual manufacturing feedback data for analyzing problems in virtual production and optimizing the manufacturing process. Adjustments are made according to the process problems (such as manufacturing errors, assembly difficulties, etc.) fed back during the virtual simulation process. Data analysis is performed on the feedback of manufacturing equipment and processes to evaluate problems and potential optimization points in actual production. By analyzing the feedback data, the impact of different manufacturing schemes on production costs and efficiency is evaluated, thereby optimizing the production process and reducing manufacturing costs. Adjust the material selection or process details according to the feedback to ensure manufacturing feasibility and optimize the production process. After the analysis is completed, the virtual manufacturing feedback data will be used for further adjustment and optimization of the design model. According to the virtual manufacturing feedback data, the design parameters in the 3D parametric design model are adjusted, such as geometric structure, material selection, manufacturing process, etc., to improve manufacturing feasibility and product performance. Adjust the process flow that affects manufacturing in the design model to ensure that the processing process of each component can be completed efficiently in the actual production environment. The optimized 3D parametric design model will be output as a programmable model file to generate a standard 3D design document. The optimized 3D parametric design model is output as a programmable model file to generate a 3D intelligent design document, including the following: export standardized design file formats (such as STEP, IGES, STL, etc.) that can be directly used by the production system for subsequent manufacturing and quality control, generate detailed programmable data, including processing paths, manufacturing process parameters, assembly sequence, etc., for direct use by intelligent manufacturing equipment. Output a full set of 3D design documents, including key data such as geometric parameters, material parameters, mechanical properties, manufacturing processes, etc., for reference in production and optimization. 3D intelligent design documents can not only be used for product manufacturing, but also for subsequent maintenance and optimization, supporting the full life cycle management of products.

[0079] Preferably, step S1 comprises the following steps:

[0080] Step S11: Acquire intelligent round baler design data;

[0081] Step S12: performing data cleaning on the intelligent round baler design data to generate intelligent round baler design cleaning data; performing outlier filtering on the intelligent round baler design cleaning data to generate intelligent round baler design filtering data;

[0082] Step S13: performing data dimensionality reduction on the intelligent round baler design filtering data to generate intelligent round baler design dimensionality reduction data; performing redundant feature compression on the intelligent round baler design dimensionality reduction data to generate intelligent round baler simplified design data;

[0083] Step S14: Standardizing the simplified design data of the intelligent round baler to generate simplified design data of the standard intelligent round baler.

[0084] In an embodiment of the present invention, the design data of the intelligent round baler, including mechanical component parameters, material properties and expected performance indicators, is obtained through CAD software or a data acquisition system. Identify and delete duplicate data. Fill in missing values ​​to ensure data integrity. The specific process is as follows: First, conduct a comprehensive data review of the design cleaning data of the intelligent round baler, scan the data set field by field, and mark the data items containing null values ​​or NaN (Not a Number). For each field with missing values, perform the following analysis: confirm whether the missing values ​​appear randomly or have a specific pattern (such as a time period, a certain category of data), analyze the correlation between the missing values ​​and other variables, and check whether these missing values ​​can be inferred or predicted through other fields. According to the characteristics of the missing values, different filling methods are selected: mean or median filling is applicable to continuous variables, and the global mean or median of the field is used to fill in the missing values. This method is applicable to situations where the missing values ​​account for a small proportion and there are no obvious abnormalities. If the missing values ​​show certain continuity or time series characteristics, linear interpolation, spline interpolation and other methods can be used to infer the filling value. Use the KNN (K nearest neighbor) algorithm to fill in the missing values ​​based on the data of similar samples. This method is applicable to data with obvious nonlinear characteristics. For numeric fields, directly use programming tools (such as the fillna() method of the Pandas library) to fill in the gaps with the global mean or median. For time series or continuous variables, apply interpolation algorithms (such as linear interpolation or spline interpolation) and use the previous and next data points to predict the fill value. Use the KNN model to use the data of neighboring samples as a reference for similarity judgment and missing value filling. Verify whether the filled data is consistent with other fields, such as relevance, logical relationship, etc. Compare the distribution with the data before filling to ensure that the filled data does not cause significant deviations in the overall distribution. Delete or replace outliers through upper and lower thresholds to generate intelligent round baler design filter data. Apply principal component analysis (PCA) or t-SNE and other techniques to reduce the dimensionality of the intelligent round baler design filter data to reduce data complexity. The specific process is to project the data onto a new coordinate axis (principal component) to retain as much variance as possible to achieve the purpose of reducing the dimension. The following is the detailed process of PCA: Calculate the covariance matrix of the intelligent round baler design filter data: The covariance matrix is ​​a measure of the correlation between features, which is used to determine the internal structure of the data. The formula .in is the data sample, is the mean, For the number of samples, the covariance matrix is ​​decomposed by eigenvalue to obtain the eigenvector and the corresponding eigenvalue: the eigenvector represents the new projection direction, that is, the principal component; the eigenvalue represents the variance explained on the principal component. The larger the eigenvalue, the stronger the explanatory power of the principal component for the data. According to the size of the eigenvalue, the first few principal components with higher explained total variance are selected (usually the principal components with cumulative explained variance of 85%-95% are selected). These principal components are new features after dimensionality reduction, which greatly reduces the complexity of the data. The standardized data is projected into the selected principal component space to generate low-dimensional intelligent round baler design data. These newly generated features are linear combinations of the original data, with fewer dimensions but retaining most of the information, that is, the output: generate intelligent round baler design data after dimensionality reduction based on PCA. Identify and remove redundant features, retain features that have a significant impact on design performance, and generate streamlined design data for intelligent round balers. Standardize the simplified design data of smart round balers (for example, Z-score standardization or Min-Max normalization) so that each feature has the same scale for subsequent analysis and modeling, generate standard simplified design data of smart round balers, and ensure that the data can be compared and analyzed under unified standards.

[0085] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0086] Step S21: extracting three-dimensional geometric features from the simplified design data of the standard intelligent round baler to obtain three-dimensional geometric feature data of the intelligent round baler, wherein the three-dimensional geometric feature extraction includes shape features, size features and curvature features;

[0087] Step S22: performing multi-dimensional structural characteristic calculation on the three-dimensional geometric characteristic data of the intelligent round baler to generate multi-dimensional structural mechanical characteristic data of the intelligent round baler;

[0088] Step S23: performing high-dimensional spatial feature optimization on the simplified design data of the standard intelligent round baler according to the three-dimensional geometric feature data of the intelligent round baler and the multi-dimensional structural mechanical property data of the intelligent round baler to generate high-dimensional spatial feature data; performing dynamic feature mapping on the high-dimensional spatial feature data to generate multi-dimensional spatial optimization feature data of the intelligent round baler;

[0089] Step S24: construct a parametric design model based on the simplified design data of the standard intelligent round baler; use the multi-dimensional space optimization feature data of the intelligent round baler to perform nonlinear parameter mapping on the parametric design model to generate parametric constraint optimization data.

[0090] In an embodiment of the present invention, shape features, such as contours, edges, and surface features, are extracted from the simplified design data of a standard intelligent round baler by using geometric analysis algorithms (such as contour extraction and shape descriptors). Key dimensional parameters, such as length, width, height, and the relative positions of the components are calculated. The curvature features of the three-dimensional geometric body are extracted using curvature analysis methods (such as Gaussian curvature and mean curvature) to form three-dimensional geometric feature data of the intelligent round baler. The finite element analysis (FEA) method is applied to calculate the multi-dimensional structural mechanical properties of the extracted three-dimensional geometric feature data. The calculation includes but is not limited to mechanical properties such as stress, strain, stiffness, and strength to generate multi-dimensional structural mechanical property data of the intelligent round baler. Based on the three-dimensional geometric feature data and multi-dimensional structural mechanical property data of the intelligent round baler, an optimization algorithm (such as a genetic algorithm or a particle swarm optimization) is used to optimize the high-dimensional spatial features of the simplified design data of the standard intelligent round baler to generate high-dimensional spatial feature data. Through dynamic mapping technology (such as principal component analysis or autoencoder), the high-dimensional space feature data is processed to generate multi-dimensional space optimization feature data of the intelligent round baler. Taking the autoencoder as an example, the specific process is to first standardize the multi-dimensional space feature data of the intelligent round baler to ensure that the mean of each feature is 0 and the standard deviation is 1. Autoencoders usually consist of two parts: an encoder and a decoder. The encoder gradually maps high-dimensional data to a low-dimensional space. Through several layers of neural networks, the input data is compressed and the dimension is gradually reduced. The decoder maps the low-dimensional data back to the original dimension and tries to reconstruct the input data. In this way, the network is able to learn important features in high-dimensional data and filter out irrelevant information. When training an autoencoder, the input data itself is used as the label of the training set, and the network tries to minimize the error (such as mean square error) between the input data and the decoder output: ,in is the original input data, is the output of the decoder. After training, the output of the encoder part is the representation of the data in a low-dimensional space, that is, the multi-dimensional space optimization feature data of the intelligent round baler. This low-dimensional representation can retain the main structural features of the data while greatly reducing the complexity. The dimensionality reduction effect is optimized by adjusting the hyperparameters such as the number of encoder layers and the number of neurons. Generally, autoencoders are more suitable for processing nonlinear and high-dimensional data than PCA, and can extract more complex features. The reconstruction error is used to evaluate the dimensionality reduction effect of the autoencoder. A small reconstruction error indicates that the network effectively extracts the core features of the original data. The structure of the data after dimensionality reduction can also be verified by visualizing the distribution of low-dimensional data (such as a two-dimensional or three-dimensional scatter plot). Based on the streamlined design data of the standard intelligent round baler, a parametric design model is constructed using parametric modeling tools (such as the parametric function in CAD software) to define the model parameters and their constraints. Using the multi-dimensional space optimization feature data of the intelligent round baler, nonlinear parameter mapping is performed on the parametric design model to generate parametric constraint optimization data to ensure that the design meets performance and reliability requirements in actual applications.

[0091] Preferably, step S22 includes the following steps:

[0092] Step S221: performing mechanical property correlation analysis on the three-dimensional geometric feature data of the intelligent round baler to generate geometric feature mechanical mapping data;

[0093] Step S222: performing structural characteristic hierarchical analysis on the geometric characteristic mechanical mapping data to generate structural characteristic hierarchical data of the intelligent round baler; performing multi-layer mechanical characteristic calculation on the structural characteristic hierarchical data of the intelligent round baler to obtain multi-layer structural mechanical characteristic data of the intelligent round baler, wherein the multi-layer mechanical characteristic calculation includes local stress calculation, bending strength calculation and dynamic load calculation;

[0094] Step S223: Performing mechanical environment simulation on the mechanical characteristic data of the multi-layer structure of the intelligent round baler to generate mechanical environment simulation data;

[0095] Step S224: Calibrate the overall mechanical characteristics of the multi-layer structure mechanical characteristics data of the intelligent round baler according to the mechanical environment simulation data, so as to generate the multi-dimensional structural mechanical characteristics data of the intelligent round baler.

[0096] In an embodiment of the present invention, a mechanical property correlation analysis is performed based on the three-dimensional geometric feature data of the intelligent round baler by adopting regression analysis or machine learning methods. Specifically, regression analysis is taken as an example: the geometric feature data contains variables of many different dimensions, such as the diameter, thickness, curvature, surface area, etc. of the round baler. In regression analysis, it is not necessary for all features to have a significant impact on the mechanical properties, so feature selection is required: the correlation between each geometric feature and the mechanical properties can be calculated by methods such as the Pearson correlation coefficient or the Spearman correlation, and features with low correlation can be eliminated. If the number of features is too large, consider using methods such as PCA to reduce the dimension and extract the main feature components. If the relationship between the geometric features and the mechanical properties is close to linear, use a linear regression model with the formula: ,in are the predicted mechanical properties, is the geometric feature, is the intercept, is the regression coefficient, is the error term. If there is a nonlinear relationship between the geometric characteristics and the mechanical properties, polynomial regression can be used, assuming a quadratic relationship: . The model is trained by historical data sets, and the coefficients (regression weights) in the model are adjusted using methods such as gradient descent or least squares. The data set is usually divided into a training set and a validation set to evaluate the generalization ability of the model. Common division methods include cross-validation (such as k-fold cross-validation). The performance of the model is measured by evaluation indicators such as mean square error (MSE), determination coefficient (R2R^2R2), etc. Use the trained regression model to predict new geometric feature data and generate geometric feature mechanical mapping data, that is, how geometric features affect the mechanical properties of the equipment. These data can help further optimize the design. Identify the relationship between geometric features and mechanical properties, and generate geometric feature mechanical mapping data, including corresponding mechanical indicators such as stress and strain. Layer the geometric feature mechanical mapping data, identify structural characteristics at different levels, such as the outer shell layer, support layer and internal structure, and generate layered data of the structural characteristics of the intelligent round baler. Local stress calculation, bending strength calculation and dynamic load calculation are performed for the layered structural characteristic data. Specific methods include: using finite element analysis (FEA) tools to perform local stress analysis on different components. Calculate the bending strength of each layer of material through bending theory to ensure that the design meets the load requirements. Simulate the dynamic load in the working environment, evaluate the performance of the structure under actual use conditions, and generate the mechanical property data of the multi-layer structure of the intelligent round baler. Use computer simulation software (such as ANSYS, ABAQUS, etc.) to simulate the mechanical environment and simulate the mechanical response under real working conditions. Consider a variety of environmental factors (such as temperature, humidity, and vibration) to generate mechanical environment simulation data to reflect the performance of the design under different conditions. According to the mechanical environment simulation data, the overall mechanical property calibration of the mechanical property data of the multi-layer structure of the intelligent round baler is performed. Use correction algorithms (such as Kalman filtering or least squares method) to optimize the mechanical property data of the multi-layer structure to ensure its accuracy and reliability in practical applications. Finally, the multi-dimensional structural mechanical property data of the intelligent round baler is generated to provide a basis for subsequent design improvements and performance optimization.

[0097] Preferably, step S24 includes the following steps:

[0098] Step S241: extracting key part features from the simplified design data of the standard intelligent round baler to obtain key part feature data of the intelligent round baler; performing spatial mapping on the key part feature data of the intelligent round baler to generate a parametric design model;

[0099] Step S242: performing nonlinear parameter mapping on the multi-dimensional spatial optimization characteristic data of the intelligent round baler to generate spatial parameter mapping data of the intelligent round baler;

[0100] Step S243: introducing physical constraints into the spatial parameter mapping data of the intelligent round baler to generate physical constraint data of the intelligent round baler, wherein the physical constraint introduction includes the introduction of load capacity limitation, the introduction of running speed limitation and the introduction of geometric size limitation;

[0101] Step S244: Based on the physical constraint data of the intelligent round baler, the intelligent round baler space parameter mapping data is used to tune the design parameters of the parametric design model to generate parametric constraint optimization data.

[0102] In an embodiment of the present invention, by identifying key parts (such as drive system, support structure and connector) in the design of intelligent round baler, extracting their geometric features and performance indicators, and generating key parts feature data of intelligent round baler. Using computer-aided design (CAD) software, the key parts feature data are spatially mapped, and the corresponding parametric design model is constructed to facilitate subsequent analysis and optimization. The specific process includes that the premise of parametric design in CAD is to collect sufficiently accurate key parts feature data. For intelligent round balers, the data of key parts include: geometric data: such as geometric dimensions such as length, width, height, angle, etc. of the parts. Mechanical property data: such as stress, strain, stiffness, strength, etc. Kinematic data: describes the dynamic characteristics of the moving parts such as displacement, velocity, acceleration, etc. The geometric data is directly mapped to the CAD model, for example, the dimensional parameters (length, radius, etc.) of the parts are mapped to the three-dimensional space of the CAD model, or the mechanical or motion characteristics are mapped to the dynamic properties or material properties of the model. For example, stress and strain data are mapped to the deformation behavior of the material. Before spatial mapping, a coordinate system (usually a three-dimensional coordinate system) needs to be defined in the CAD software to ensure that the data of all key components can be correctly positioned in space. The specific steps are as follows: Select a reasonable origin as the reference point of the design model, and set the reference plane (such as XY plane, YZ plane) to determine the relative position of each component. According to the position and geometric characteristics of the components, the collected feature data (such as length, width, height, angle, etc.) are projected to specific points, lines, surfaces or bodies in three-dimensional space. The geometric part of the CAD model is constructed through feature data. For example: Use CAD tools to generate corresponding geometric objects such as points, lines, planes or surfaces from the input geometric data (such as coordinate points, edge lines, angles). Create basic three-dimensional solid models such as cylinders and cubes based on the input geometric feature data (such as radius, thickness, height). Parametric design refers to controlling the shape, size and structure of the model through parameters rather than manually adjusting the model. The key points include: defining the key parameters of the model in the CAD software, such as length, width, thickness, angle, etc. By setting formulas or constraints, ensure that changes in one parameter can automatically update other related parameters. For example, if the radius of the round baler drum changes, the length parameter of the strapping strap also needs to be adjusted accordingly. Enter geometric feature data such as length, radius, angle, etc. as parameters into the CAD software. Each geometric entity (such as a line, plane, circle, etc.) has its properties defined by parametric constraints. In parametric design, geometric constraints (such as symmetry, perpendicularity) and dimensional constraints (such as length, angle) are used to ensure that the parts of the model remain interrelated. For example, the distance between two parallel faces of a component can be set parameterically. Based on the input parameters, a preliminary 3D model is generated. In the design of an intelligent round baler, the shape of components such as the drum and transmission can be controlled by parameterization to control their size and structure.The important feature of parametric modeling is to express the design intent. For example, if the thickness of a component affects the length of another component, the length can be automatically adjusted when the thickness changes through parameter association. If the material properties affect the mechanical properties, the material selection of the component can also be controlled through parametric design. With parametric models, automated rules can be set in the design. For example, the size of the component can be automatically adjusted under certain conditions to ensure that the mechanical performance requirements are met. Once the parametric model is completed, the designer can quickly iterate the model by adjusting key parameters. For example, when optimizing a certain part of a round baler, only specific parameters need to be adjusted, and the entire model will be automatically updated without remodeling. Nonlinear parameter mapping is performed on the multi-dimensional spatial optimization feature data of the intelligent round baler, and machine learning or interpolation methods are used to generate the spatial parameter mapping data of the intelligent round baler to achieve more complex parameter relationships in the design model. According to the material properties and design requirements, the maximum load capacity of the intelligent round baler is determined and introduced as a design constraint. The speed limit of the intelligent round baler under actual operating conditions is analyzed to ensure that the design is within a safe range. The geometric size limits of key parts are set according to the design specifications and actual manufacturing capabilities. The above physical constraints are integrated to generate the physical constraint data of the intelligent round baler, providing a basis for subsequent design optimization. Based on the physical constraint data of the intelligent round baler, the design parameters of the parametric design model are optimized using spatial parameter mapping data. Optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.) are used to adjust the design parameters to ensure that the physical constraints are met while optimizing the design performance, and finally generate parametric constraint optimization data.

[0103] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0104] Step S31: analyzing the design material characteristics of the simplified design data of the standard intelligent round baler to generate material characteristic data of the intelligent round baler;

[0105] Step S32: Based on the parameterized constraint optimization data, the three-dimensional geometric feature data of the intelligent round baler, the multi-dimensional structural mechanical property data of the intelligent round baler, and the material property data of the intelligent round baler are used to perform virtual modeling of the parts of the intelligent round baler to generate three-dimensional modeling data of the parts of the intelligent round baler;

[0106] Step S33: performing dynamic characteristic simulation on the three-dimensional modeling data of the intelligent round baler parts to generate dynamic characteristic simulation data; performing operating environment simulation on the dynamic characteristic simulation data to generate intelligent round baler modeling environment adaptability data;

[0107] Step S34: dynamically reconstructing the three-dimensional virtual model of the three-dimensional modeling data of the parts of the intelligent round baler through the modeling environment adaptability data and dynamic characteristic simulation data of the intelligent round baler to generate a three-dimensional virtual simulation model of the intelligent round baler.

[0108] In the embodiment of the present invention, material information related to the intelligent round baler is collected, including parameters such as strength, toughness, ductility and corrosion resistance. The selected materials are analyzed using a material database and standardized test methods (such as tensile tests and impact tests) to generate material property data of the intelligent round baler. Based on the parametric constraint optimization data, the key parts of the intelligent round baler are virtually modeled in combination with the three-dimensional geometric feature data of the intelligent round baler, the multi-dimensional structural mechanical property data of the intelligent round baler and the material property data of the intelligent round baler. Modeling is performed using CAD software to generate three-dimensional modeling data of the parts of the intelligent round baler for subsequent analysis and simulation. The dynamic characteristics of the three-dimensional modeling data of the parts of the intelligent round baler are simulated, and the dynamic characteristics simulation data are generated using finite element analysis (FEA) software or multi-body dynamics simulation tools (such as ADAMS, ANSYS, etc.), and the response of the components under different loads, speeds and motion conditions is analyzed. On the basis of the dynamic simulation, the operating environment simulation is performed, and external factors such as temperature, humidity, and vibration are considered to generate the environmental adaptability data of the intelligent round baler modeling. According to the modeling environment adaptability data and dynamic characteristics simulation data of the intelligent round baler, the three-dimensional modeling data of the parts of the intelligent round baler are dynamically reconstructed. Virtual reality (VR) technology or augmented reality (AR) technology is used to convert the simulation results into a dynamic three-dimensional virtual model, and a three-dimensional virtual simulation model of the intelligent round baler is generated to facilitate design verification and operation training. The specific process includes collecting two types of data for the intelligent round baler: Modeling environment adaptability data: describes the adaptability of the intelligent round baler under different environmental conditions (temperature, humidity, vibration, pressure, etc.) to ensure the accuracy of the model in different environments. Dynamic characteristics simulation data: dynamic response data of each component of the round baler obtained based on mechanical simulation, such as acceleration, velocity, displacement, force and other parameters. The preliminary three-dimensional geometric model of the intelligent round baler is constructed using the design blueprint, CAD data or scanned geometric data of the intelligent round baler. This includes: Component-level modeling: establish basic three-dimensional models for the key components of the intelligent round baler (such as baling device, hydraulic system, transmission system, etc.). Material properties and texture application: according to the previous material selection, the three-dimensional model of each component is given corresponding material properties and surface textures to ensure the realism of the model in the virtual environment. Introduce the dynamic characteristic simulation data into the three-dimensional model and dynamically reconstruct the model. The steps are as follows: Dynamic data binding: Bind the simulation data to the corresponding components in the three-dimensional model, especially the dynamic response of the moving parts (such as the transmission system, rolling shaft, etc.). Environmental adaptability impact simulation: According to the environmental adaptability data, simulate the working state of the round baler in different environments, such as material expansion at high temperature or changes in component movement resistance at low temperature. Visualization of the dynamic simulation process: Through the simulation engine (such as Unity, Unreal Engine, etc.), the movement process of the components is realized, and the behavior of the components under load, such as deformation, displacement, vibration, etc., is dynamically displayed.On the basis of dynamic 3D modeling, dynamic simulation tools are used to further optimize the dynamic behavior of the model, including: Force analysis: Use finite element analysis (FEA) tools to analyze the force of each component of the model during dynamic operation, and optimize the component structure to reduce stress concentration or excessive deformation. Motion trajectory optimization: According to the results of dynamic simulation, the motion trajectory and speed change of the key moving parts of the intelligent round baler are optimized to ensure the smoothness and efficiency of operation. Collision detection and correction: Perform collision detection on the model in a virtual environment to avoid accidental interference between different components, and adjust the geometric structure of the model when necessary. Through the simulation environment setting, different external environmental conditions (such as temperature, humidity, wind speed, etc.) are introduced into the 3D model to simulate the operation effect of the round baler in different environments: The influence of temperature on material properties: such as high temperature causes metal expansion, and the brittleness of materials increases at low temperatures. Vibration and shock effects: Simulate the influence of vibration on the connection of components during operation, and analyze the stability and vibration resistance of the structure. Import the simulated and optimized 3D model into the VR / AR system for immersive simulation display. The main steps are as follows: In order to improve the operating efficiency, the 3D model needs to be optimized and compressed to reduce the number of polygons while maintaining the fineness of the model. The 3D model is exported to a format supported by the VR / AR system (such as FBX, OBJ, etc.) to ensure efficient rendering of the model in the virtual environment. In the virtual reality environment, the 3D model and simulation results of the intelligent round baler are immersively displayed through VR headsets (such as Oculus Rift, HTC Vive, etc.). Specific operations include: users can freely observe the structure and dynamic operation process of the round baler in the virtual environment, adjust the viewing angle, zoom in and out in real time, and even perform interactive operations (such as starting and stopping the equipment, changing the load, etc.). The simulation engine simulates the working state of the round baler in real time, detects faults, and provides visual feedback (such as parts with excessive force turn red, indicating stress concentration). The 3D virtual simulation model is applied to the augmented reality scene, and the intelligent round baler in the real environment is superimposed and displayed through mobile devices (such as smartphones, tablets or AR glasses). The steps are as follows: Using the camera of the AR device, the virtual 3D model is superimposed on the real environment, and the user can "see" the operation process and simulation results of the intelligent round baler in reality. Based on the dynamic simulation results, the AR system can display the dynamic changes of components in real scenes (such as deformation or failure under simulated loading conditions), helping operators identify potential problems. Through the above process, a three-dimensional virtual simulation model of the intelligent round baler is generated.

[0109] Preferably, performing dynamic boundary simulation on material property mapping data of the intelligent round baler according to multi-dimensional structural mechanical property data of the intelligent round baler through parameterized constraint optimization data includes:

[0110] According to the multi-dimensional structural mechanical characteristics data of the intelligent round baler, the material attribute mapping data of the intelligent round baler is matched with the component materials to generate the material matching data of the parts of the intelligent round baler;

[0111] Perform core part screening on the material matching data of the parts of the intelligent round baler to obtain the core part screening data of the intelligent round baler, wherein the core part screening data of the intelligent round baler includes the tying part data, the driving part data and the supporting structure part data;

[0112] The drum force characteristics of the bundling part data are simulated by using the parameterized constraint optimization data to generate the drum force characteristics simulation data; the gear vibration characteristics of the driving part data are simulated based on the parameterized constraint optimization data to generate the gear force characteristics simulation data; the bracket response characteristics of the supporting structure part data are simulated by using the parameterized constraint optimization data to generate the bracket response characteristics simulation data;

[0113] The simulation data of the drum force characteristics, the gear force characteristics and the bracket response characteristics are integrated to generate three-dimensional modeling data of the intelligent round baler parts.

[0114] In the embodiment of the present invention, the mechanical performance requirements of each component are analyzed according to the multi-dimensional structural mechanical characteristic data of the intelligent round baler, and the material selection is performed according to the working conditions and functional requirements of each component, and the material matching data of the parts of the intelligent round baler are generated to ensure that the selected materials can meet the indicators such as strength, stiffness and toughness. The specific analysis process includes extracting the multi-dimensional structural mechanical characteristic data of each component from the design of the intelligent round baler. These data include: Load type and size: such as compression, tension, shear or torsion. Stress and strain distribution: understand the stress concentration area and strain degree of the component when working. Displacement and deformation characteristics: analyze the deformation generated under the working state. Vibration and shock: the vibration frequency and shock to which the component is subjected in a dynamic working environment. According to the extracted mechanical characteristic data, the mechanical performance requirements of each component in the actual working environment are analyzed, mainly including the following aspects: Strength requirements: the ability of the component to withstand the maximum stress to ensure that it will not be permanently deformed or damaged due to the load. Stiffness requirements: the ability of the component to resist deformation when subjected to force. The higher the stiffness, the smaller the deformation of the component. Toughness requirements: the ability of the component to absorb energy when subjected to impact or dynamic load to ensure that it will not break suddenly under impact load. Fatigue performance: The ability of a component to resist fatigue under long-term cyclic loads, especially for mechanical components that are constantly in operation. The mechanical performance requirements of each component will be quantified based on its specific working environment, functional requirements, and mechanical properties. For example, key load-bearing components have high requirements for strength and stiffness, while high-speed rotating components have higher requirements for toughness and fatigue performance. Combined with the working environment of each component, further analyze the specific requirements for its material selection: Temperature effect: Temperature changes in the working environment will affect the performance of the material, and it is necessary to select materials that can remain stable in high or low temperature environments. Corrosion and wear: If the component is in a high wear or corrosion environment, it is necessary to select materials with wear or corrosion resistance. Dynamic load and impact conditions: For components that are subjected to dynamic loads or high impact, the toughness and impact resistance of the material are particularly critical. According to the mechanical performance requirements of each component, combined with the mechanical performance data in the material library, a material matching analysis is performed. The key analysis steps are as follows: Strength matching: For load-bearing components, select materials with sufficiently high tensile strength and yield strength to ensure that no plastic deformation or failure occurs when subjected to maximum load. For example, steel or high-strength alloys can be used for key load-bearing frames. Stiffness matching: For components with small deformation requirements, select materials with high elastic modulus to ensure that the displacement and deformation during operation are within the allowable range. For example, titanium alloy or carbon fiber composite materials have high stiffness and are suitable for components with high precision requirements. Toughness matching: For components with frequent dynamic loads or impact loads, select materials with high fracture toughness and high fatigue limit to ensure that the material does not break under repeated impact or fatigue loads. For example, spring steel or aluminum alloy is suitable for components that are subjected to high impact.Analyze the material matching data of the parts of the intelligent round baler, identify the core parts that have the greatest impact on the performance, and generate the screening data of the core parts of the intelligent round baler, including: analyzing the pressure and friction of the material during the baling process. Evaluate the stress conditions of the gears and transmission components in the drive system. Determine the performance of the support structure under load. Use parameterized constraint optimization data to simulate the drum force characteristics of the baling part, analyze the force distribution and stress concentration during the baling process, and generate drum force characteristics simulation data. Based on the parameterized constraint optimization data, simulate the gear vibration characteristics of the drive part, evaluate the vibration response of the gear at different speeds and loads, and generate gear force characteristics simulation data. Through the parameterized constraint optimization data, simulate the support structure's bracket response characteristics, analyze the deformation and stress distribution of the bracket under load, and generate bracket response characteristics simulation data. Integrate the drum force characteristics simulation data, gear force characteristics simulation data, and bracket response characteristics simulation data, and use data fusion methods (such as weighted average or principal component analysis) to generate three-dimensional modeling data of the parts of the intelligent round baler. Ensure that the integrated data can reflect the performance of the core parts of the intelligent round baler under actual working conditions.

[0115] Preferably, step S4 comprises the following steps:

[0116] Step S41: Modeling and designing the parametric design model according to the three-dimensional virtual simulation model of the intelligent round baler to generate a three-dimensional parametric design model of the intelligent round baler;

[0117] Step S42: performing production adaptability detection on the three-dimensional parametric design model of the intelligent round baler to generate three-dimensional parameter production adaptability data; performing virtual manufacturing simulation on the three-dimensional parametric design model of the intelligent round baler through the three-dimensional parameter production adaptability data to generate virtual manufacturing feedback data;

[0118] Step S43: Outputting the three-dimensional parametric design model of the intelligent round baler into a programmable model file through virtual manufacturing feedback data, thereby generating a three-dimensional intelligent design document.

[0119] In an embodiment of the present invention, a parametric design model is modeled and associated according to a three-dimensional virtual simulation model of an intelligent round baler. Using CAD software or a parametric modeling tool, the features and parameters of the three-dimensional virtual simulation model are embedded into the design model to generate a three-dimensional parametric design model of the intelligent round baler. Ensure that the design model can dynamically respond to parameter changes and support rapid modification and optimization. Perform production adaptability testing on the three-dimensional parametric design model of the intelligent round baler to evaluate its feasibility in the actual production process, including material selection, processing technology, assembly requirements, etc., generate three-dimensional parameter production adaptability data, and provide matching analysis of design and manufacturing. Based on the three-dimensional parameter production adaptability data, perform virtual manufacturing simulation, simulate the actual production process, and generate virtual manufacturing feedback data to facilitate identification of potential problems and optimization of the design. The specific virtual manufacturing simulation process includes selecting a simulation platform and tools. You can choose professional virtual manufacturing simulation software, such as: Autodesk Moldflow: for injection molding simulation. ANSYS Workbench: a general simulation platform that can be used for multi-physics field simulation. Siemens NX: for machining and manufacturing process simulation. Simulink / Matlab: System-level simulation of the production process can be performed. According to the design requirements of the intelligent round baler, select the appropriate manufacturing process simulation. Virtual manufacturing processes usually include: Material processing simulation: such as cutting, stamping, welding, casting and other process simulations. Assembly process simulation: Simulate the assembly process of multiple parts to check the matching accuracy. Heat treatment process simulation: Simulate the effect of heat treatment on material properties (such as hardness and toughness). Quality control simulation: Check whether the parts meet the design standards during the manufacturing process. Virtual manufacturing simulation can use finite element analysis (FEM), discrete element method (DEM), or multi-body dynamics (MBD) and other methods to simulate different manufacturing processes. FEM is used to analyze the force and stress distribution of the material during the processing process, and to predict processing deformation, cutting temperature, surface roughness, etc. For example, in the metal processing process of the round baler, the heat generated when the tool contacts the workpiece, the material removal rate and the processing surface state are simulated. The connection, fit and movement between components are simulated through multi-body dynamics to evaluate assembly accuracy, interference and force conditions. For example, in the assembly simulation of the intelligent round baler, the bolt connection of the components, the spring force and the displacement changes of the adjacent components are simulated to detect the assembly accuracy. During the simulation, the manufacturing process parameters such as tool speed, welding current, heat treatment temperature, etc. are gradually adjusted to find the optimal combination of production parameters to ensure production quality and efficiency. The following virtual manufacturing feedback data are obtained through simulation: Manufacturing error feedback data: data such as dimensional error, geometric deformation and surface defects obtained after simulation. Mechanical property feedback data: such as the stress, strain and mechanical property changes of the material during the processing. Process optimization feedback data: the impact of process parameters on the final product quality and processing efficiency.Using virtual manufacturing feedback data, the 3D parametric design model of the intelligent round baler is modified and optimized to ensure that the design meets production requirements. The final design model is converted into a programmable model file format (such as STL, STEP, IGES, etc.) and the 3D intelligent design document is output to facilitate subsequent manufacturing, assembly and quality control.

[0120] In this specification, a three-dimensional parametric design system for an intelligent round baler is provided, which is used to execute the above-mentioned three-dimensional parametric design method for an intelligent round baler. The three-dimensional parametric design system for an intelligent round baler includes:

[0121] A data processing module is used to obtain the design data of the intelligent round baler; compress the redundant features of the design data of the intelligent round baler to generate the simplified design data of the intelligent round baler; and standardize the simplified design data of the intelligent round baler to generate the simplified design data of the standard intelligent round baler;

[0122] The structural mapping module is used to extract the three-dimensional geometric features of the simplified design data of the standard intelligent round baler to obtain the three-dimensional geometric feature data of the intelligent round baler; perform multi-dimensional structural characteristic calculations on the three-dimensional geometric feature data of the intelligent round baler to generate multi-dimensional structural mechanical characteristic data of the intelligent round baler; construct a parametric design model; use the multi-dimensional structural mechanical characteristic data of the intelligent round baler to perform nonlinear parameter mapping on the parametric design model to generate parametric constraint optimization data;

[0123] The three-dimensional reconstruction module is used to analyze the design material characteristics of the simplified design data of the standard intelligent round baler and generate the material characteristic data of the intelligent round baler; based on the parametric constraint optimization data, the three-dimensional geometric feature data of the intelligent round baler, the multi-dimensional structural mechanical characteristic data of the intelligent round baler and the material characteristic data of the intelligent round baler are used to perform virtual modeling of the parts and generate the three-dimensional modeling data of the parts of the intelligent round baler; the three-dimensional virtual model is dynamically reconstructed for the three-dimensional modeling data of the parts of the intelligent round baler to generate a three-dimensional virtual simulation model of the intelligent round baler;

[0124] The feedback output module is used to perform virtual manufacturing simulation on the three-dimensional virtual simulation model of the intelligent round baler according to the three-dimensional virtual simulation model of the intelligent round baler, and generate virtual manufacturing feedback data; the three-dimensional parametric design model of the intelligent round baler is output as a programmable model file through the virtual manufacturing feedback data, thereby generating a three-dimensional intelligent design document.

[0125] The beneficial effect of the present invention is that the design data of the intelligent round baler is simplified through redundant feature compression and data standardization, making subsequent processing more efficient, reducing the waste of computing resources caused by redundant information, and improving data processing speed and design accuracy. The three-dimensional geometric feature extraction and multi-dimensional structural mechanical property calculation ensure the accuracy of the shape, size and mechanical properties of the intelligent round baler, and provide reliable basic data for subsequent model optimization. The parametric design model is nonlinearly mapped based on the mechanical property data to generate parametric constraint optimization data. This process improves the flexibility and adaptability of the design model and ensures its optimized performance under different conditions. The material properties, geometric properties and structural mechanical properties are comprehensively applied to virtual modeling to generate accurate three-dimensional modeling data, ensuring the authenticity and physical consistency of the model, which helps to improve the structural strength and durability of the product. The dynamic reconstruction of the three-dimensional virtual model of the intelligent round baler provides a visual verification method for the product. Through real-time simulation and dynamic reconstruction, the behavior under complex operating environments can be simulated, providing important feedback for further optimization of the design. Through virtual manufacturing simulation, potential problems in the manufacturing process can be discovered in advance, the generation of design defects can be reduced, and the generated virtual manufacturing feedback data provides a reliable basis for optimizing the design model, improving manufacturing accuracy and product quality. By generating three-dimensional intelligent design documents through virtual manufacturing feedback, automated output is achieved, errors in manual document compilation are reduced, and standardized, easy-to-understand technical documents are provided for production, design, quality inspection and other links. The entire process from design, optimization, modeling, simulation to feedback and document output forms a closed-loop design optimization process, which improves the design efficiency, manufacturing accuracy and production adaptability of intelligent round balers. Therefore, the present invention improves the redundancy, efficiency and accuracy in the three-dimensional parametric design of intelligent round balers through data simplification, linkage between geometric and mechanical properties, combination of material properties and virtual simulation feedback optimization.

[0126] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0127] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A three-dimensional parametric design method for an intelligent round baler, characterized in that: The following steps are involved: Step S1: acquiring intelligent round baler design data; compressing redundant features of the intelligent round baler design data to generate simplified design data of the intelligent round baler; and standardizing the simplified design data of the intelligent round baler to generate simplified design data of standard intelligent round baler; Step S2: extracting three-dimensional geometric features from the simplified design data of the standard intelligent round baler to obtain three-dimensional geometric feature data of the intelligent round baler; Perform multi-dimensional structural characteristic calculations on the three-dimensional geometric feature data of the intelligent round baler to generate multi-dimensional structural mechanical characteristic data of the intelligent round baler; and construct a parametric design model; The multi-dimensional structural mechanical characteristics data of the intelligent round baler are used to perform nonlinear parameter mapping on the parametric design model to generate parametric constraint optimization data; Step S2 includes the following steps: Step S21: extracting three-dimensional geometric features from the simplified design data of the standard intelligent round baler to obtain three-dimensional geometric feature data of the intelligent round baler, wherein the three-dimensional geometric feature extraction includes shape features, size features and curvature features; Step S22: performing multi-dimensional structural characteristic calculation on the three-dimensional geometric characteristic data of the intelligent round baler to generate multi-dimensional structural mechanical characteristic data of the intelligent round baler; Step S22 includes the following steps: Step S221: performing mechanical property correlation analysis on the three-dimensional geometric feature data of the intelligent round baler to generate geometric feature mechanical mapping data; Step S222: performing structural characteristic hierarchical analysis on the geometric characteristic mechanical mapping data to generate structural characteristic hierarchical data of the intelligent round baler; performing multi-layer mechanical characteristic calculation on the structural characteristic hierarchical data of the intelligent round baler to obtain multi-layer structural mechanical characteristic data of the intelligent round baler, wherein the multi-layer mechanical characteristic calculation includes local stress calculation, bending strength calculation and dynamic load calculation; Step S223: Performing mechanical environment simulation on the mechanical characteristic data of the multi-layer structure of the intelligent round baler to generate mechanical environment simulation data; Step S224: Calibrate the overall mechanical characteristics of the multi-layer structure of the intelligent round baler according to the mechanical environment simulation data, so as to generate the multi-dimensional structural mechanical characteristics data of the intelligent round baler; Step S23: performing high-dimensional spatial feature optimization on the simplified design data of the standard intelligent round baler according to the three-dimensional geometric feature data of the intelligent round baler and the multi-dimensional structural mechanical property data of the intelligent round baler to generate high-dimensional spatial feature data; performing dynamic feature mapping on the high-dimensional spatial feature data to generate multi-dimensional spatial optimization feature data of the intelligent round baler; Step S24: construct a parametric design model based on the simplified design data of the standard intelligent round baler; use the multi-dimensional space optimization feature data of the intelligent round baler to perform nonlinear parameter mapping on the parametric design model to generate parametric constraint optimization data; Step S24 includes the following steps: Step S241: extracting key part features from the simplified design data of the standard intelligent round baler to obtain key part feature data of the intelligent round baler; performing spatial mapping on the key part feature data of the intelligent round baler to generate a parametric design model; Step S242: performing nonlinear parameter mapping on the multi-dimensional spatial optimization characteristic data of the intelligent round baler to generate spatial parameter mapping data of the intelligent round baler; Step S243: introducing physical constraints into the spatial parameter mapping data of the intelligent round baler to generate physical constraint data of the intelligent round baler, wherein the physical constraint introduction includes the introduction of load capacity limitation, the introduction of running speed limitation and the introduction of geometric size limitation; Step S244: optimizing the design parameters of the parametric design model using the spatial parameter mapping data of the intelligent round baler based on the physical constraint data of the intelligent round baler, and generating parameterized constraint optimization data; Step S3: Analyze the design material characteristics of the simplified design data of the standard intelligent round baler to generate the material characteristic data of the intelligent round baler; perform virtual modeling of parts using the three-dimensional geometric feature data of the intelligent round baler, the multi-dimensional structural mechanical characteristic data of the intelligent round baler, and the material characteristic data of the intelligent round baler based on the parametric constraint optimization data to generate the three-dimensional modeling data of the parts of the intelligent round baler; dynamically reconstruct the three-dimensional virtual model of the three-dimensional modeling data of the parts of the intelligent round baler to generate a three-dimensional virtual simulation model of the intelligent round baler; Step S4: Perform virtual manufacturing simulation on the three-dimensional virtual simulation model of the intelligent round baler according to the three-dimensional virtual simulation model of the intelligent round baler to generate virtual manufacturing feedback data; output the three-dimensional parametric design model of the intelligent round baler as a programmable model file through the virtual manufacturing feedback data, thereby generating a three-dimensional intelligent design document.

2. The three-dimensional parameterized design method for an intelligent round baler according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire intelligent round baler design data; Step S12: performing data cleaning on the intelligent round baler design data to generate intelligent round baler design cleaning data; performing outlier filtering on the intelligent round baler design cleaning data to generate intelligent round baler design filtering data; Step S13: performing data dimensionality reduction on the intelligent round baler design filtering data to generate intelligent round baler design dimensionality reduction data; performing redundant feature compression on the intelligent round baler design dimensionality reduction data to generate intelligent round baler streamlined design data; Step S14: Standardizing the simplified design data of the intelligent round baler to generate simplified design data of the standard intelligent round baler.

3. The three-dimensional parameterized design method for an intelligent round baler according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: analyzing the design material characteristics of the simplified design data of the standard intelligent round baler to generate material characteristic data of the intelligent round baler; Step S32: Based on the parameterized constraint optimization data, the three-dimensional geometric feature data of the intelligent round baler, the multi-dimensional structural mechanical property data of the intelligent round baler, and the material property data of the intelligent round baler are used to perform virtual modeling of the parts of the intelligent round baler to generate three-dimensional modeling data of the parts of the intelligent round baler; Step S33: performing dynamic characteristic simulation on the three-dimensional modeling data of the intelligent round baler parts to generate dynamic characteristic simulation data; performing operating environment simulation on the dynamic characteristic simulation data to generate intelligent round baler modeling environment adaptability data; Step S34: dynamically reconstructing the three-dimensional virtual model of the three-dimensional modeling data of the parts of the intelligent round baler through the modeling environment adaptability data and dynamic characteristic simulation data of the intelligent round baler to generate a three-dimensional virtual simulation model of the intelligent round baler.

4. The three-dimensional parameterized design method for an intelligent round baler according to claim 3, characterized in that: Step S32 includes the following steps: Step S321: using CAD technology to perform overall 3D geometric modeling on the three-dimensional geometric feature data of the intelligent round baler to generate overall 3D geometric modeling data; Step S322: performing surface meshing on the overall 3D geometric modeling data to generate overall 3D surface mesh data; performing mesh area calculation on the overall 3D surface mesh data to obtain 3D surface mesh area data; comparing the 3D surface mesh area data with a preset mesh area threshold, and when the 3D surface mesh area data is less than the preset mesh area threshold, marking the corresponding 3D surface mesh as key mesh data; Step S323: grid-blocking the key grid data to generate key grid block data; modeling and refining the key grid block data to generate key grid refinement modeling data; integrating the overall 3D geometric modeling data and the key grid refinement modeling data to generate intelligent round baler geometric modeling data; Step S324: Use the material property data of the intelligent round baler to perform material property mapping on the geometric modeling data of the intelligent round baler to generate material property mapping data of the intelligent round baler; perform dynamic boundary simulation on the material property mapping data of the intelligent round baler according to the multi-dimensional structural mechanical property data of the intelligent round baler through parameterized constraint optimization data to generate three-dimensional modeling data of the parts of the intelligent round baler.

5. The three-dimensional parameterized design method for an intelligent round baler according to claim 4, characterized in that: Through parameterized constraint optimization data, dynamic boundary simulation of intelligent round baler material property mapping data is performed according to the multi-dimensional structural mechanical characteristics data of the intelligent round baler, including: According to the multi-dimensional structural mechanical characteristics data of the intelligent round baler, the material attribute mapping data of the intelligent round baler is matched with the component materials to generate the material matching data of the parts of the intelligent round baler; Perform core part screening on the material matching data of the parts of the intelligent round baler to obtain the core part screening data of the intelligent round baler, wherein the core part screening data of the intelligent round baler includes the tying part data, the driving part data and the supporting structure part data; The drum force characteristics of the bundling part data are simulated by using the parameterized constraint optimization data to generate the drum force characteristics simulation data; the gear vibration characteristics of the driving part data are simulated based on the parameterized constraint optimization data to generate the gear force characteristics simulation data; the bracket response characteristics of the supporting structure part data are simulated by using the parameterized constraint optimization data to generate the bracket response characteristics simulation data; The simulation data of the drum force characteristics, the gear force characteristics and the bracket response characteristics are integrated to generate three-dimensional modeling data of the intelligent round baler parts.

6. The three-dimensional parameterized design method for an intelligent round baler according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: Modeling and designing the parametric design model according to the three-dimensional virtual simulation model of the intelligent round baler to generate a three-dimensional parametric design model of the intelligent round baler; Step S42: performing production adaptability detection on the three-dimensional parametric design model of the intelligent round baler to generate three-dimensional parameter production adaptability data; performing virtual manufacturing simulation on the three-dimensional parametric design model of the intelligent round baler through the three-dimensional parameter production adaptability data to generate virtual manufacturing feedback data; Step S43: Outputting the three-dimensional parametric design model of the intelligent round baler into a programmable model file through virtual manufacturing feedback data, thereby generating a three-dimensional intelligent design document.

7. A three-dimensional parametric design system for intelligent round balers, characterized in that: For executing the three-dimensional parametric design method for an intelligent round baler as claimed in claim 1, the three-dimensional parametric design system for an intelligent round baler comprises: A data processing module is used to obtain the design data of the intelligent round baler; compress the redundant features of the design data of the intelligent round baler to generate the simplified design data of the intelligent round baler; and standardize the simplified design data of the intelligent round baler to generate the simplified design data of the standard intelligent round baler; The structural mapping module is used to extract the three-dimensional geometric features of the simplified design data of the standard intelligent round baler to obtain the three-dimensional geometric feature data of the intelligent round baler; perform multi-dimensional structural characteristic calculations on the three-dimensional geometric feature data of the intelligent round baler to generate multi-dimensional structural mechanical characteristic data of the intelligent round baler; construct a parametric design model; use the multi-dimensional structural mechanical characteristic data of the intelligent round baler to perform nonlinear parameter mapping on the parametric design model to generate parametric constraint optimization data; The three-dimensional reconstruction module is used to analyze the design material characteristics of the simplified design data of the standard intelligent round baler and generate the material characteristic data of the intelligent round baler; based on the parametric constraint optimization data, the three-dimensional geometric feature data of the intelligent round baler, the multi-dimensional structural mechanical characteristic data of the intelligent round baler and the material characteristic data of the intelligent round baler are used to perform virtual modeling of the parts and generate the three-dimensional modeling data of the parts of the intelligent round baler; the three-dimensional virtual model is dynamically reconstructed for the three-dimensional modeling data of the parts of the intelligent round baler to generate a three-dimensional virtual simulation model of the intelligent round baler; The feedback output module is used to perform virtual manufacturing simulation on the three-dimensional virtual simulation model of the intelligent round baler according to the three-dimensional virtual simulation model of the intelligent round baler, and generate virtual manufacturing feedback data; the three-dimensional parametric design model of the intelligent round baler is output as a programmable model file through the virtual manufacturing feedback data, thereby generating a three-dimensional intelligent design document.

Images